The present disclosure generally relates to CMOS image sensors, and more specifically aims at a CMOS image sensor capable of implementing compressive sensing methods.
A CMOS image sensor generally comprises a plurality of pixels arranged in rows and in columns. Each pixel comprises a photodiode used in reverse mode, having its junction capacitor discharged by a photocurrent according to a received light intensity. The illumination level received by a pixel is measured by measurement of a quantity representative of the voltage across the photodiode at selected times, including the end of a period, called integration period, before and after which the pixel is reset by recharging of its photodiode.
Conventionally, in an image acquisition phase, for each pixel of the sensor, an output value representative of the illumination level received by the pixel during the integration is read, digitized, and stored in digital form. To decrease the quantity of digital data to be stored/processed downstream of the sensor, the acquisition phase is often followed by a phase of compressing the digitized image.
This conventional method of acquiring an entire digitized image, followed by a phase of compressing the digitized image, has several disadvantages. In particular, acquiring an entire digitized image is relatively long, which is a limit to the increase of image acquisition rates. Further, such an acquisition of an entire digitized image results in a relatively high electric power consumption by the read and analog-to-digital conversion circuits of the sensor. Further, the phase of compressing the digitized image may be relatively long and complex, and implies providing a digital signal processing unit dedicated to such a compression at the sensor output, possibly on the same chip as the sensor. These various disadvantages particularly raise an issue in systems with significant constraints relative to the acquisition and the compression of images in terms of processing speed and/or of electric power consumption.
To attempt overcoming all or part of these disadvantages, so-called compressive sensing methods have already been provided, where the compression phase is implemented in analog mode, upstream of the analog-to-digital converter(s), in combination with the acquisition phase.
Some of the compressive sensing methods, which will be called pixel binning compressive sensing methods, enable to acquire and to simultaneously compress the image by providing, instead of reading and digitizing an output value representative of an illumination level individually received by each pixel, to make a plurality of non-coherent measurements, each based on a measurement support comprising a plurality of sensor pixels, for example, all the sensor pixels, or a subset of sensor pixels. Each measurement is a weighted sum of the brightness levels received by the different pixels of a measurement support. The weighting coefficients are randomly or pseudo-randomly generated. Such coefficients may be binary (0 or 1), which makes the implementation of the weighted sum operations easier. On acquisition of an image, a plurality of measurements with different sets of weighting coefficients are generally provided on a same measurement support, it being understood that, to obtain a compressive effect, the total number of measurements performed on the sensor should be smaller than the number of sensor pixels. It is thus possible to decrease the image acquisition time and the electric power consumption associated with the acquisition, particularly due to the fact that less data are read and digitized by the sensor. Further, digital compressive processing operations, subsequent to the acquisition, may be decreased or suppressed.
The original image can be reconstructed from the compressed image and the array of weighting coefficients used on acquisition. Such a reconstruction uses the sparseness of the original image in a specific decomposition base, for example, in a discrete cosine base or in a wavelet base.
Theories of compressive sensing with a pixel binning have been discussed in detail in various publications, for example, in article “An Introduction To Compressive Sensing” by Emmanuel J. Candès et al.
Further, CMOS image sensor architectures using pixel binning compressive sensing have been described in articles “Block-Based Compressive Sensing in a CMOS Image Sensor”, by M. R. Dadkhah et al., and “CMOS Image Sensor With Per-Column ΣΔ ADC and Programmable Compressed Sensing” by Yusuke Oike et al.
Other compressive sensing methods, which will be called methods of compressive sensing with no pixel binning, enable to simultaneously acquire and compress the image by providing, instead of reading and digitizing an output value representative of an illumination level received by each sensor pixel, randomly or pseudo-randomly selecting a number of pixels on the sensor, and only reading and digitizing the output values of the selected pixels. Unlike pixel binning compressive sensing, the values of the selected pixels are here individually read and digitized (without being added to other pixel values). The compression rate then is the ratio of the number of pixels selected in read mode during the acquisition to the total number of sensor pixels.
The original image can be reconstructed from the compressed image, provided to know the positions, in the sensor pixel array, of the pixels selected in read mode during the acquisition. As in the case of pixel binning compressive sensing, the reconstruction uses the sparseness of the original image in a specific decomposition base. Such a reconstruction may be carried out by methods identical or similar to those used in pixel binning compressive sensing.
An example of a system implementing a compressive sensing with no pixel binning is disclosed in article “Chaotic Scan: A Low Complexity Video Transmission System for Efficiently Sending Relevant Image Features”, by R. Dogaru et al.
It should be noted that in the field of compressive sensing (with or without pixel binning), the use of pseudo-random generators, that is, generators having a predictive behavior, has the advantage of enabling, at the time of the reconstruction of the original image, to generate for a second time the non-coherent binary values used in the acquisition—as weighting coefficients in the case of a pixel binning compressive sensing —or as a mask for selecting the pixels to be read from in the case of a compressive sensing without pixel binning. The original image can thus be reconstructed without having to transmit, with the compressed image, the non-coherent binary values used during the sensing.
There is a need for a CMOS image sensor capable of implementing compressive sensing methods, such a sensor at least partly improving certain aspects of prior art sensors using compressive sensing. In particular, there is a need for a CMOS sensor providing, for each equivalent image quality, a better compression rate than prior art sensors using compressive sensing or, for an equivalent compression rate, a better image quality than prior art sensors using compressive sensing.